Congeners key to water quality improvements

Led by Ohio State University, new research recently published in the journal Harmful Algae suggests that toxins produced by harmful algal blooms (HAB) in Lake Erie may be “overestimated in earlier in summer and underestimated later” in the season when the inexpensive analysis tool ELISA is used.

Justin Chaffin, lead author of the study is a research coordinator at OSU’s Stone Laboratory, where researchers seek to develop a more accurate HAB toxicity forecast for the great lake. After water treatment, Lake Erie supplies drinking water for an estimated 11 million people in the United States and Canada.

Analysis revealed a strong relationship between the toxicity in Lake Erie and nitrogen. Different microcystin congeners also generate different amounts of toxins. Researchers want to determine the “location and abundance of the different congeners” to better inform water management as well as recreation & beach management.

Over two summer seasons, June through September in 2018 and 2019, water quality samples were collected at 15 sites between Maumee Bay to the Central Basin. Specific microcystin congeners were identified and their concentration over time recorded; changing nutrient levels in the water were also recorded.

Unlike the analysis tool ELISA, detecting congeners is more expensive. The use of advanced equipment also focuses on specific concentration of congeners instead of the overall concentration.

Chaffin also co-authored another study that use timeseries data on toxin concentrations (from existing ELISA measurements without a focus on congeners), water currents, and an increase in HAB toxin production to forecast microcystin concentrations and create a 7-day map of the algal bloom. He added, “…If you add biology data to the simulation, you can get a better prediction of where the highest toxin concentrations will be.”

To assist water quality managers, we facilitate the integration of water quality sampling and biological data. Our software provides holistic visualization of water chemistry results and aquatic biotic indices over time. More meaningful views of water quality indicators can be seen and analyzed. Advanced analytics enable our software users to use quality-assured datasets to model future nutrient concentration levels and identify opportunities and threats to clean water operations.